Detecting protein-RNA interactions is challenging?both experimentally and computationally? because RNA transcripts are large in number, diverse in cellular location and function. As a result, many RNA-binding proteins (RBPs) and their cognate motifs are likely unknown or uncharacterized in humans as well as other model organisms. With increasing number of RBPs implicated in human diseases, there is an urgent need for identifying and mapping functional and phenotypic information for RBPs as well as to complete a map of the protein-RNA interaction network. The objective here is to establish a robust computational technique that integrates expression associations with sequence as well as several RBP centric features for genome-scale prediction of binding motifs for hundreds of human RBPs to facilitate the elucidation of their tissue-specific post-transcriptional networks. At the completion of this project, we expect to have developed the most advanced tool for predicting human RBP motifs and methods as well as resources which can facilitate the construction of tissue-specific RBP-RNA networks. Our central hypothesis, supported by our initial genome-scale computational study and assessment by comparative analysis of known RBP binding motifs is that, since many RBPs are involved in different stages of RNA metabolism, exon expression level associations with an RBP and other exon related features can be very powerful in identifying the binding motifs of an RBP in a tissue-specific manner. The proposed integrated approach to experimentally validate several binding motifs using CLIP-seq and to deconvolute global posttranscriptional networks in specific cell/tissue types, using genome-wide data from protein protection assays (POP-seq) will significantly enhance our capability of uncovering network dynamics of RBPs in cell types and tissues. Such high-quality predictions based on experimental validations, resulting from all the Aims which will be made public, can become a venue for future experimental follow up to dissect the role of these important regulatory molecules in different tissues and disease states. The proposed studies will make an impact in the field as the first large-scale computational mapping of protein-RNA interaction networks in the human tissues by taking our ability to predict RBP targets to the next level. The complementary experience and expertise of investigators will make this project successful.